About

The Journal of Deep Learning in Genomic Data Analysis (JDLGDA) is a peer-reviewed publication dedicated to advancing research at the intersection of deep learning and genomic data analysis. With the rapid advancements in sequencing technologies and the exponential growth of genomic data, JDLGDA provides a platform for researchers, bioinformaticians, and computational biologists to explore innovative deep learning approaches that enable comprehensive analysis, interpretation, and visualization of genomic datasets. The journal covers a wide range of topics, including variant calling, gene expression analysis, regulatory element prediction, pathway analysis, and personalized medicine. JDLGDA welcomes original research articles, review papers, and methodological developments that demonstrate the utility and effectiveness of deep learning techniques in addressing challenges in genomic data analysis. By fostering collaboration between deep learning researchers and genomics experts, JDLGDA aims to accelerate scientific discovery, advance our understanding of the genetic basis of diseases, and facilitate the development of precision medicine approaches.